SoroushVahidi/scidocs huggingface dataset:
Fuzzy clustering with artificial bee colony algorithm
(2010)

In this work, performance of the Artificial Bee Colony Algorithm which is a recently proposed algorithm, has been tested on fuzzy clustering. We applied the Artificial Bee Colony (ABC) Algorithm fuzzy clustering to classify different data sets; Cancer, Diabetes and Heart from UCI database, a collection of classification benchmark problems. The results indicate that the performance of Artificial Bee Colony Optimization Algorithm is successful in fuzzy clustering.
A PRAM and NAND flash hybrid architecture for high-performance embedded storage subsystems
(2008)

NAND flash-based storage is widely used in embedded systems due to its numerous benefits: low cost, high density, small form factor and so on. However, NAND flash-based storage is still suffering from serious performance degradation for random or small size write access. This degradation mainly comes from the physical constraints of NAND flash: erase-before-program and different unit size of erase and program operations. To overcome these constraints, we propose to use PRAM (Phase-change RAM) which supports advanced features: fast byte access capability and no requirement for erase-before-program.
 In this paper, we focus on developing a high-performance NAND flash-based storage system by maximally exploiting the advanced feature of PRAM, in terms of performance and wearing out. To do this, we first propose a new hybrid storage architecture which consists of PRAM and NAND flash. Second, we devise two novel software schemes for the proposed hybrid storage architecture; FSMS (File System Metadata Separation) and hFTL (hybrid Flash Translation Layer). Finally, we demonstrate that our hybrid architecture increases the performance up to 290% and doubles the lifespan compared to the existing NAND flash only storage systems.
New developments in our understanding of acne pathogenesis and treatment.
(2009)

Interest in sebaceous gland physiology and its diseases is rapidly increasing. We provide a summarized update of the current knowledge of the pathobiology of acne vulgaris and new treatment concepts that have emerged in the last 3 years (2005-2008). We have tried to answer questions arising from the exploration of sebaceous gland biology, hormonal factors, hyperkeratinization, role of bacteria, sebum, nutrition, cytokines and toll-like receptors (TLRs). Sebaceous glands play an important role as active participants in the innate immunity of the skin. They produce neuropeptides, excrete antimicrobial peptides and exhibit characteristics of stem cells. Androgens affect sebocytes and infundibular keratinocytes in a complex manner influencing cellular differentiation, proliferation, lipogenesis and comedogenesis. Retention hyperkeratosis in closed comedones and inflammatory papules is attributable to a disorder of terminal keratinocyte differentiation. Propionibacterium acnes, by acting on TLR-2, may stimulate the secretion of cytokines, such as interleukin (IL)-6 and IL-8 by follicular keratinocytes and IL-8 and -12 in macrophages, giving rise to inflammation. Certain P. acnes species may induce an immunological reaction by stimulating the production of sebocyte and keratinocyte antimicrobial peptides, which play an important role in the innate immunity of the follicle. Qualitative changes of sebum lipids induce alteration of keratinocyte differentiation and induce IL-1 secretion, contributing to the development of follicular hyperkeratosis. High glycemic load food and milk may induce increased tissue levels of 5alpha-dihydrotestosterone. These new aspects of acne pathogenesis lead to the considerations of possible customized therapeutic regimens. Current research is expected to lead to innovative treatments in the near future.
Short-term meditation training improves attention and self-regulation.
(2007)

Recent studies suggest that months to years of intensive and systematic meditation training can improve attention. However, the lengthy training required has made it difficult to use random assignment of participants to conditions to confirm these findings. This article shows that a group randomly assigned to 5 days of meditation practice with the integrative body-mind training method shows significantly better attention and control of stress than a similarly chosen control group given relaxation training. The training method comes from traditional Chinese medicine and incorporates aspects of other meditation and mindfulness training. Compared with the control group, the experimental group of 40 undergraduate Chinese students given 5 days of 20-min integrative training showed greater improvement in conflict scores on the Attention Network Test, lower anxiety, depression, anger, and fatigue, and higher vigor on the Profile of Mood States scale, a significant decrease in stress-related cortisol, and an increase in immunoreactivity. These results provide a convenient method for studying the influence of meditation training by using experimental and control methods similar to those used to test drugs or other interventions.
The benefits of being present: mindfulness and its role in psychological well-being.
(2003)

Mindfulness is an attribute of consciousness long believed to promote well-being. This research provides a theoretical and empirical examination of the role of mindfulness in psychological well-being. The development and psychometric properties of the dispositional Mindful Attention Awareness Scale (MAAS) are described. Correlational, quasi-experimental, and laboratory studies then show that the MAAS measures a unique quality of consciousness that is related to a variety of well-being constructs, that differentiates mindfulness practitioners from others, and that is associated with enhanced self-awareness. An experience-sampling study shows that both dispositional and state mindfulness predict self-regulated behavior and positive emotional states. Finally, a clinical intervention study with cancer patients demonstrates that increases in mindfulness over time relate to declines in mood disturbance and stress.
Does mindfulness training improve cognitive abilities? A systematic review of neuropsychological findings.
(2011)

Mindfulness meditation practices (MMPs) are a subgroup of meditation practices which are receiving growing attention. The present paper reviews current evidence about the effects of MMPs on objective measures of cognitive functions. Five databases were searched. Twenty three studies providing measures of attention, memory, executive functions and further miscellaneous measures of cognition were included. Fifteen were controlled or randomized controlled studies and 8 were case-control studies. Overall, reviewed studies suggested that early phases of mindfulness training, which are more concerned with the development of focused attention, could be associated with significant improvements in selective and executive attention whereas the following phases, which are characterized by an open monitoring of internal and external stimuli, could be mainly associated with improved unfocused sustained attention abilities. Additionally, MMPs could enhance working memory capacity and some executive functions. However, many of the included studies show methodological limitations and negative results have been reported as well, plausibly reflecting differences in study design, study duration and patients' populations. Accordingly, even though findings here reviewed provided preliminary evidence suggesting that MMPs could enhance cognitive functions, available evidence should be considered with caution and further high quality studies investigating more standardized mindfulness meditation programs are needed.
Effectiveness of a meditation-based stress reduction program in the treatment of anxiety disorders.
(1992)

OBJECTIVE
This study was designed to determine the effectiveness of a group stress reduction program based on mindfulness meditation for patients with anxiety disorders.


METHOD
The 22 study participants were screened with a structured clinical interview and found to meet the DSM-III-R criteria for generalized anxiety disorder or panic disorder with or without agoraphobia. Assessments, including self-ratings and therapists' ratings, were obtained weekly before and during the meditation-based stress reduction and relaxation program and monthly during the 3-month follow-up period.


RESULTS
Repeated measures analyses of variance documented significant reductions in anxiety and depression scores after treatment for 20 of the subjects--changes that were maintained at follow-up. The number of subjects experiencing panic symptoms was also substantially reduced. A comparison of the study subjects with a group of nonstudy participants in the program who met the initial screening criteria for entry into the study showed that both groups achieved similar reductions in anxiety scores on the SCL-90-R and on the Medical Symptom Checklist, suggesting generalizability of the study findings.


CONCLUSIONS
A group mindfulness meditation training program can effectively reduce symptoms of anxiety and panic and can help maintain these reductions in patients with generalized anxiety disorder, panic disorder, or panic disorder with agoraphobia.
The clinical use of mindfulness meditation for the self-regulation of chronic pain
(1985)

Ninety chronic pain patients were trained in mindfulness meditation in a 10-week Stress Reduction and Relaxation Program. Statistically significant reductions were observed in measures of present-moment pain, negative body image, inhibition of activity by pain, symptoms, mood disturbance, and psychological symptomatology, including anxiety and depression. Pain-related drug utilization decreased and activity levels and feelings of self-esteem increased. Improvement appeared to be independent of gender, source of referral, and type of pain. A comparison group of pain patients did not show significant improvement on these measures after traditional treatment protocols. At follow-up, the improvements observed during the meditation training were maintained up to 15 months post-meditation training for all measures except present-moment pain. The majority of subjects reported continued high compliance with the meditation practice as part of their daily lives. The relationship of mindfulness meditation to other psychological methods for chronic pain control is discussed.
Decision Tree Discovery
(1999)

We describe the two most commonly used systems for induction of decision trees for classi cation: C4.5 and CART. We highlight the methods and di erent decisions made in each system with respect to splitting criteria, pruning, noise handling, and other di erentiating features. We describe how rules can be derived from decision trees and point to some di erence in the induction of regression trees. We conclude with some pointers to advanced techniques, including ensemble methods, oblique splits, grafting, and coping with large data.
Development of Fire alarm system using Raspberry Pi and Arduino Uno
(2013)

The proposed Fire alarm system is a real-time monitoring system that detects the presence of smoke in the air due to fire and captures images via a camera installed inside a room when a fire occurs. The embedded systems used to develop this fire alarm system are Raspberry Pi and Arduino Uno. The key feature of the system is the ability to remotely send an alert when a fire is detected. When the presence of smoke is detected, the system will display an image of the room state in a webpage. The system will need the user confirmation to report the event to the Firefighter using Short Message Service (SMS). The advantage of using this system is it will reduce the possibility of false alert reported to the Firefighter. The camera will only capture an image, so this system will consume a little storage and power.
On the Influence of Emotional Valence Shifts on the Spread of Information in Social Networks
(2017)

In this paper, we present a study on 4.4 million Twitter messages related to 24 systematically chosen real-world events. For each of the 4.4 million tweets, we first extracted sentiment scores based on the eight basic emotions according to Plutchik's wheel of emotions. Subsequently, we investigated the effects of shifts in the emotional valence on the spread of information. We found that in general OSN users tend to conform to the emotional valence of the respective real-world event. However, we also found empirical evidence that prospectively negative real-world events exhibit a significant amount of shifted emotions in the corresponding tweets (i.e. positive messages). To explain this finding, we use the theory of social connection and emotional contagion. To the best of our knowledge, this is the first study that provides empirical evidence for the undoing hypothesis in online social networks (OSNs). The undoing hypothesis postulates that positive emotions serve as an antidote during negative events.
Comparison between Auditory and Visual Simple Reaction Times
(2010)

Objective: The purpose of this study was to find out whether the simple reaction time was faster for auditory or visual stimulus and the factors responsible for improving the performance of the athlete. Methodology: 14 subjects were assigned randomly into groups consisting of 2 members. Both the members from each group performed both the visual and auditory tests. The tests were taken from the DirectRT software program from a laptop. The DirectRT software consists of Testlabvisual and Testlabsounds to test the reaction times to visual and auditory stimuli. The 2 members from each group completed both the visual and auditory reaction times, the data was taken and the mean reaction time was calculated excluding the first and last values. Results: The results show that the mean visual reaction time is around 331 milliseconds as compared to the mean auditory reaction time of around 284 milliseconds. Conclusion: This shows that the auditory reaction time is faster than the visual reaction time. And also males have faster reaction times when compared to females for both auditory as well as visual stimuli.
Maximizing Air Gap and Efficiency of Magnetic Resonant Coupling for Wireless Power Transfer Using Equivalent Circuit and Neumann Formula
(2011)

The progress in the field of wireless power transfer in the last few years is remarkable. With recent research, transferring power across large air gaps has been achieved. Both small and large electric equipment have been proposed, e.g., wireless power transfer for small equipment (mobile phones and laptops) and for large equipment (electric vehicles). Furthermore, replacing every cord with wireless power transfer is proposed. The coupled mode theory was proposed in 2006 and proven in 2007. Magnetic and electric resonant couplings allow power to traverse large air gaps with high efficiency. This technology is closely related to electromagnetic induction and has been applied to antennas and resonators used for filters in communication technology. We have studied these phenomena and technologies using equivalent circuits, which is a more familiar format for electrical engineers than the coupled mode theory. In this paper, we analyzed the relationship between maximum efficiency air gap using equivalent circuits and the Neumann formula and proposed equations for the conditions required to achieve maximum efficiency for a given air gap. The results of these equations match well with the results of electromagnetic field analysis and experiments.
Five-Level Inverter for Renewable Power Generation System
(2013)

In this paper, a five-level inverter is developed and applied for injecting the real power of the renewable power into the grid to reduce the switching power loss, harmonic distortion, and electromagnetic interference caused by the switching operation of power electronic devices. Two dc capacitors, a dual-buck converter, a full-bridge inverter, and a filter configure the five-level inverter. The input of the dual-buck converter is two dc capacitor voltage sources. The dual-buck converter converts two dc capacitor voltage sources to a dc output voltage with three levels and balances these two dc capacitor voltages. The output voltage of the dual-buck converter supplies to the full-bridge inverter. The power electronic switches of the full-bridge inverter are switched in low frequency synchronous with the utility voltage to convert the output voltage of the dual-buck converter to a five-level ac voltage. The output current of the five-level inverter is controlled to generate a sinusoidal current in phase with the utility voltage to inject into the grid. A hardware prototype is developed to verify the performance of the developed renewable power generation system. The experimental results show that the developed renewable power generation system reaches the expected performance.
A Systematic Approach of Dataset Definition for a Supervised Machine Learning Using NFR Framework
(2018)

Non-functional requirements describe important constraints upon the software development and should therefore be considered and specified as early as possible during the system analysis. Effective elicitation of requirements is arguably among the most important of the resulting recommended RE practices. Recent research has shown that artificial intelligence techniques such as Machine Learning and Text Mining perform the automatic extraction and classification of quality attributes from text documents with relevant results. This paper aims to define a systematic process of dataset generation through NFR Framework catalogues improving the NFR's classification process using Machine Learning techniques. A well-known dataset (Promise) was used to evaluate the precision of our approach reaching interesting results. Regarding to security and performance we obtained a precision and recall ranging between ~85% and ~98%. And we achievement a F1 above ~79% when classified the security, performance and usability together.
Improving malware detection time by using RLE and N-gram
(2017)

Malware is a widespread problem and despite the common use of anti-virus software, the diversity of malware is still increasing. A major challenge facing the anti-virus industry is how to effectively detect thousands of malware samples that are received every day. In this paper, a novel approach based Run Length Encoding (RLE) algorithm and n-gram are proposed to improve malware detect on dynamic analysis of based on API sequences.
Mapping gray matter development: Implications for typical development and vulnerability to psychopathology
(2010)

Recent studies with brain magnetic resonance imaging (MRI) have scanned large numbers of children and adolescents repeatedly over time, as their brains develop, tracking volumetric changes in gray and white matter in remarkable detail. Focusing on gray matter changes specifically, here we explain how earlier studies using lobar volumes of specific anatomical regions showed how different lobes of the brain matured at different rates. With the advent of more sophisticated brain mapping methods, it became possible to chart the dynamic trajectory of cortical maturation using detailed 3D and 4D (dynamic) models, showing spreading waves of changes evolving through the cortex. This led to a variety of time-lapse films revealing characteristic deviations from normal development in schizophrenia, bipolar illness, and even in siblings at genetic risk for these disorders. We describe how these methods have helped clarify how cortical development relates to cognitive performance, functional recovery or decline in illness, and ongoing myelination processes. These time-lapse maps have also been used to study effects of genotype and medication on cortical maturation, presenting a powerful framework to study factors that influence the developing brain.
A Secure Communication Architecture for Distributed Microgrid Control
(2015)

Microgrids are a key component in the evolution of the power grid. Microgrids are required to operate in both grid connected and standalone island mode using local sources of power. A major challenge in implementing microgrids is the communications and control to support transition from grid connected mode and operation in island mode. Here, we propose a secure communication architecture to support microgrid operation and control. A security model, including network, data, and attack models, is defined and a security protocol to address the real-time communication needs of microgrids is proposed. The implementation of the proposed security scheme is discussed and its performance evaluated using theoretical and co-simulation analysis, which shows it to be superior to existing protocols.
Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images
(2018)

Malaria is a blood disease caused by the Plasmodium parasites transmitted through the bite of female Anopheles mosquito. Microscopists commonly examine thick and thin blood smears to diagnose disease and compute parasitemia. However, their accuracy depends on smear quality and expertise in classifying and counting parasitized and uninfected cells. Such an examination could be arduous for large-scale diagnoses resulting in poor quality. State-of-the-art image-analysis based computer-aided diagnosis (CADx) methods using machine learning (ML) techniques, applied to microscopic images of the smears using hand-engineered features demand expertise in analyzing morphological, textural, and positional variations of the region of interest (ROI). In contrast, Convolutional Neural Networks (CNN), a class of deep learning (DL) models promise highly scalable and superior results with end-to-end feature extraction and classification. Automated malaria screening using DL techniques could, therefore, serve as an effective diagnostic aid. In this study, we evaluate the performance of pre-trained CNN based DL models as feature extractors toward classifying parasitized and uninfected cells to aid in improved disease screening. We experimentally determine the optimal model layers for feature extraction from the underlying data. Statistical validation of the results demonstrates the use of pre-trained CNNs as a promising tool for feature extraction for this purpose.
Real-time 6D stereo Visual Odometry with non-overlapping fields of view
(2012)

In this paper, we present a framework for 6D absolute scale motion and structure estimation of a multi-camera system in challenging indoor environments. It operates in real-time and employs information from two cameras with non-overlapping fields of view. Monocular Visual Odometry supplying up-to-scale 6D motion information is carried out in each of the cameras, and the metric scale is recovered via a linear solution by imposing the known static transformation between both sensors. The redundancy in the motion estimates is finally exploited by a statistical fusion to an optimal 6D metric result. The proposed technique is robust to outliers and able to continuously deliver a reasonable measurement of the scale factor. The quality of the framework is demonstrated by a concise evaluation on indoor datasets, including a comparison to accurate ground truth data provided by an external motion tracking system.